Cloud Spanner

Cloud Spanner is a fully managed, mission-critical relational database service. It is designed to provide a scalable online transaction processing (OLTP) database with high availability and strong consistency at a global scale.

Cloud Spanner Cheat Sheet – Features, Pricing & Use Cases

~ Last updated on : ~ jayendrapatil

Google Cloud Spanner

🆕 Major Updates (2024-2026)

  • Spanner Editions (2024): New tier-based pricing – Standard, Enterprise, and Enterprise Plus editions replacing legacy pricing.
  • Multi-Model Database (2024-2025): Spanner Graph, Full-Text Search, and Vector Search capabilities added.
  • Spanner Omni (2026 Preview): Run Spanner on-premises, across clouds, or on a laptop.
  • Columnar Engine (2025): Real-time analytics on operational data without impacting transactional workloads.
  • Managed Autoscaler (2025 GA): Automatic scaling with independent read-only replica scaling.
  • Tiered Storage (2025 GA): SSD and HDD storage tiers within same instance (~80% cost reduction for cold data).
  • Cloud Spanner is a fully managed, mission-critical relational database service
  • Cloud Spanner provides a scalable online transaction processing (OLTP) database with high availability and strong consistency at a global scale.
  • Cloud Spanner provides traditional relational semantics like schemas, ACID transactions and SQL interface
  • Cloud Spanner provides Automatic, Synchronous replication within and across regions for high availability (99.999%)
  • Spanner now handles over 6 billion queries per second at peak and more than 17 exabytes of data
  • Cloud Spanner benefits
    • OLTP (Online Transactional Processing)
    • Global scale
    • Relational data model
    • ACID/Strong or External consistency
    • Low latency
    • Fully managed and highly available
    • Automatic replication
    • Multi-model support – relational, graph, key-value, vector, and full-text search in a single database
    • AI-ready – built-in vector search, ML predictions, and Vertex AI integrations

Spanner Editions

  • Spanner offers editions, a tier-based pricing model introduced in 2024 that provides different capabilities at different price points.
  • Standard Edition
    • Comprehensive suite of established capabilities (all GA features prior to September 2024)
    • Regional instance configurations only
    • 99.99% availability SLA
    • Relational (GoogleSQL, PostgreSQL) and Key-value support
    • BigQuery federation, Data Boost, Reverse ETL
    • Standard backups, 7-day PITR, Scheduled backups
  • Enterprise Edition
    • Builds on Standard edition with multi-model capabilities
    • Regional instance configurations with optional custom read-only replicas
    • 99.99% availability SLA
    • Adds Spanner Graph, Full-text search, Vector Search (KNN & ANN)
    • Managed autoscaler, Tiered storage, Locality groups
    • Columnar engine for analytics
    • Incremental backups
  • Enterprise Plus Edition
    • Designed for most demanding workloads
    • Multi-region and dual-region instance configurations
    • Up to 99.999% availability SLA
    • All Enterprise features plus geo-partitioning
  • Committed Use Discounts (CUDs): 20% for 1-year, 40% for 3-year commitments across all editions
  • Free trial instance available (defaults to Enterprise edition on upgrade)

Cloud Spanner Architecture

Cloud Spanner ArchitectureInstance

  • Cloud Spanner Instance determines the location and the allocation of resources
  • Instance creation includes two important choices
    • Instance configuration
      • determines the geographic placement i.e. location and replication of the databases
      • Location can be regional, dual-region, or multi-regional
      • cannot be changed once selected during the creation (but instances can be moved using the Move Instance feature)
    • Compute capacity (nodes or processing units)
      • determines the amount of the instance’s serving and storage resources
      • measured in processing units (PUs) or nodes, with 1000 PUs = 1 node
      • minimum of 100 PUs for granular instances, scaling in batches of 100 PUs
      • can be updated manually or via managed autoscaler
  • Cloud Spanner distributes an instance across zones of one or more regions to provide high performance and high availability
  • Cloud Spanner instances have:
    • At least three read-write replicas of the database each in a different zone
    • Each zone is a separate isolation fault domain
    • Paxos distributed consensus protocol used for writes/transaction commits
    • Synchronous replication of writes to all zones across all regions
    • Database is available even if one zone fails (99.999% availability SLA for multi-region and 99.99% availability SLA for regional)

Regional vs Dual-Region vs Multi-Regional

  • Regional Configuration
    • Cloud Spanner maintains 3 read-write replicas, each within a different Google Cloud zone in that region.
    • Each read-write replica contains a full copy of the operational database that is able to serve read-write and read-only requests.
    • Cloud Spanner uses replicas in different zones so that if a single-zone failure occurs, the database remains available.
    • Every Cloud Spanner mutation requires a write quorum that’s composed of a majority of voting replicas. Write quorums are formed from two out of the three replicas in regional configurations.
    • Provides 99.99% availability
    • Available in Standard, Enterprise, and Enterprise Plus editions
  • Dual-Region Configuration (New – 2024)
    • Provides five 9s (99.999%) of availability with only two regions
    • Helps meet local residency requirements in geographies with only two regions
    • Zero recovery-point objective (RPO) guarantees
    • Available only in Enterprise Plus edition
  • Multi-Regional Configuration
    • Multi-region configurations allow replicating the database’s data not just in multiple zones, but in multiple zones across multiple regions
    • Additional replicas enable reading data with low latency from multiple locations close to or within the regions in the configuration.
    • As the quorum (read-write) replicas are spread across more than one region, additional network latency is incurred when these replicas communicate with each other to vote on writes.
    • Multi-region configurations enable the application to achieve faster reads in more places at the cost of a small increase in write latency.
    • Provides 99.999% availability
    • Multi-regional makes use of Paxos-based replication, TrueTime and leader election, to provide global consistency and higher availability
    • Available only in Enterprise Plus edition

Cloud Spanner - Regional vs Multi-Regional Configurations

Geo-Partitioning (New – 2024)

  • Partition table data at the row-level across the globe
  • Serves data closer to users for reduced application latency
  • Provides data residency benefits – store sensitive data within specific geographic jurisdictions
  • Optimizes costs by placing data in appropriate regions
  • Available only in Enterprise Plus edition

Replication

  • Cloud Spanner automatically gets replication at the byte level from the underlying distributed filesystem.
  • Cloud Spanner also performs data replication to provide global availability and geographic locality, with fail-over between replicas being transparent to the client.
  • Cloud Spanner creates multiple copies, or “replicas,” of the rows, then stores these replicas in different geographic areas.
  • Cloud Spanner uses a synchronous, Paxos distributed consensus protocol, in which voting replicas take a vote on every write request to ensure transactions are available in sufficient replicas before being committed.
  • Globally synchronous replication gives the ability to read the most up-to-date data from any Cloud Spanner read-write or read-only replica.
  • Cloud Spanner creates replicas of each database split
  • A split holds a range of contiguous rows, where the rows are ordered by the primary key.
  • All of the data in a split is physically stored together in the replica, and Cloud Spanner serves each replica out of an independent failure zone.
  • A set of splits is stored and replicated using Paxos.
  • Within each Paxos replica set, one replica is elected to act as the leader.
  • Leader replicas are responsible for handling writes, while any read-write or read-only replica can serve a read request without communicating with the leader (though if a strong read is requested, the leader will typically be consulted to ensure that the read-only replica has received all recent mutations)
  • Cloud Spanner automatically reshards data into splits and automatically migrates data across machines (even across datacenters) to balance load, and in response to failures.
  • Spanner’s sharding considers the parent child relationships in interleaved tables and related data is migrated together to preserve query performance
  • Read Leases (New – 2025): Read leases can improve read latency for strongly consistent data in multi-region configurations by trading off some write performance for common read-mostly workloads

Cloud Spanner Data Model

  • A Cloud Spanner Instance can contain one or more databases
  • A Cloud Spanner database can contain one or more tables
  • Tables look like relational database tables in that they are structured with rows, columns, and values, and they contain primary keys
  • Every table must have a primary key, and that primary key can be composed of zero or more columns of that table
  • Supports both GoogleSQL and PostgreSQL dialects
  • Parent-child relationships in Cloud Spanner
    • Table Interleaving
      • Table interleaving is a good choice for many parent-child relationships where the child table’s primary key includes the parent table’s primary key columns
      • Child rows are colocated with the parent rows significantly improving the performance
      • Primary key column(s) of the parent table must be the prefix of the primary key of the child table
    • Foreign Keys
      • Foreign keys are similar to traditional databases.
      • They are not limited to primary key columns, and tables can have multiple foreign key relationships, both as a parent in some relationships and a child in others.
      • The foreign key relationship does not guarantee data co-location
  • Cloud Spanner automatically creates an index for each table’s primary key
  • Secondary indexes can be created for other columns
  • Named Schemas (New – 2025): Organize and encapsulate database objects using named schema support

Multi-Model Capabilities (New – 2024/2025)

  • Spanner evolved into a multi-model database supporting multiple data models within a single instance without data movement
  • Spanner Graph
    • Native graph database capabilities using GQL (Graph Query Language)
    • Supports schemaless data for iterative development
    • Build graphs on named schema objects and SQL views
    • Graph algorithms support for connected data analysis
    • Interoperable with SQL – combine graph and relational queries
    • Available in Enterprise and Enterprise Plus editions
  • Full-Text Search
    • Built-in full-text search with tokenization and search indexes
    • Enhanced query mode with automatic synonym matching and spell correction
    • JSON indexing for accelerated queries over JSON data
    • Fuzzy search, faceted search, and substring search support
    • Available in Enterprise and Enterprise Plus editions
  • Vector Search
    • Exact K-Nearest Neighbors (KNN) search
    • Approximate Nearest Neighbors (ANN) search using Google’s ScaNN algorithm (GA in 2025)
    • Scales to more than 10 billion vectors
    • Combine vector searches with SQL and graph GQL queries for RAG applications
    • Available in Enterprise and Enterprise Plus editions
  • Key-Value (Cassandra Interface)
    • Cassandra interface allows using familiar CQL tools and syntax
    • Lift and shift Cassandra applications with virtually no changes
    • Available in all editions

Spanner AI Integration (New – 2024/2025)

  • Vector Embeddings: Generate and store vector embeddings directly in Spanner with Vertex AI integration
  • ML.PREDICT: Natural language queries using ML predictions via SQL
  • MCP Toolbox for Databases: Build agentic AI applications with Spanner
  • Agent Development Kit (ADK): Integration with Google’s ADK for Spanner
  • Vertex RAG Engine: Use Spanner as a RAG-managed database for data indexing and retrieval
  • LangChain & LlamaIndex: Framework integrations for LLM-powered applications
  • Conversational Data Agents: Build data agents with conversational analytics
  • Key AI use cases: product search and recommendations, fraud detection, identity resolution, autonomous network operations

Cloud Spanner Scaling

  • Increase the compute capacity of the instance to scale up the server and storage resources in the instance.
  • Each node (1000 PUs) provides up to 4TB of data storage (increased from 2TB in 2022)
  • Nodes provide additional compute resources to increase throughput
  • Increasing compute capacity does not increase the replica count but gives each replica more CPU and RAM, which increases the replica’s throughput (that is, more reads and writes per second can occur).
  • Start with as little as 100 processing units for granular instances (as low as $65/month)
  • Managed Autoscaler (GA – 2025)
    • Automatically scales compute capacity based on workload levels
    • Define minimum and maximum compute capacity limits
    • Supports independent scaling of read-only replicas from read-write replicas
    • Available in Enterprise and Enterprise Plus editions
  • Manual Split Points (New – 2025): Pre-split data to handle anticipated traffic spikes (e.g., flash sales, game launches)
  • Tiered Storage (GA – 2025)
    • Store data across SSD and HDD within the same instance
    • Older data automatically moves to lower cost HDD storage (~80% cheaper)
    • No API changes required to access data on different tiers
    • Available in Enterprise and Enterprise Plus editions

Spanner Analytics & BigQuery Integration (New – 2024/2025)

  • Columnar Engine (Preview – 2025)
    • Analyze vast amounts of operational data in real-time
    • Maintains global consistency, high availability, and strong transactional guarantees
    • Does not impact transactional workloads
    • Available in Enterprise and Enterprise Plus editions
  • BigQuery Integration
    • External Datasets (GA): Query live Spanner data directly in BigQuery with zero ETL
    • Materialized Views: Ultra-fast reporting using pre-computed query results on Spanner data
    • Reverse ETL: Export/stream computed insights from BigQuery to Spanner
    • Apache Iceberg Support: Join live Spanner data with Iceberg tables in BigQuery
    • Data Boost: Independent compute resources for analytics queries without impacting operational workloads

Cloud Spanner Backup & PITR

  • Cloud Spanner Backup and Restore helps create backups of Cloud Spanner databases on demand, and restore them to provide protection against operator and application errors that result in logical data corruption.
  • Backups are highly available, encrypted, and can be retained for up to a year from the time they are created.
  • Cloud Spanner point-in-time recovery (PITR) provides protection against accidental deletion or writes.
  • PITR works by letting you configure a database’s version_retention_period to retain all versions of data and schema, from a minimum of 1 hour up to a maximum of 7 days.
  • Scheduled Backups (New – 2024): Configure automatic backup schedules for databases
  • Incremental Backups (New – 2024): Only back up changes since last backup, reducing cost and time (Enterprise/Enterprise Plus editions)
  • Default Backup Schedules (New – 2025): Automatic recovery baseline from the moment a database is created
  • Schema Object Drop Protection (New – 2025): Safeguard against accidental deletion of critical tables, indexes, and columns

Performance & Isolation (New – 2025)

  • Repeatable Read Isolation (Preview): Reduces locking overhead for workloads with low read-write contention
  • Query Optimizer v8: Automated enhancements optimizing join strategies and index usage
  • Index Advisor: Analyzes query patterns and proactively suggests new indexes or identifies unused ones
  • Schema Recommendations (Preview): Scans schema design for anti-patterns like hotspot-prone primary keys
  • Leader-Aware Routing: Routes requests to the nearest leader replica for reduced latency
  • Multiplexed Sessions: Enabled by default in major SDKs for improved throughput and resource utilization
  • Spanner CLI: Bundled with gcloud for running SQL, managing sessions, and automating scripts

Spanner Omni (Preview – 2026)

  • Downloadable version of Spanner that runs in your own environment
  • Deploy on-premises data centers, across public clouds, or on a developer laptop
  • Brings Spanner’s unlimited scalability, high availability, strong consistency, and security anywhere
  • Supports connected, hybrid, multicloud, multiregion, and air-gapped configurations
  • Scales from a single machine to clusters of thousands of servers
  • Full multi-model capabilities available in Spanner Omni

Cloud Spanner Best Practices

  • Design a schema that prevents hotspots and other performance issues.
  • For optimal write latency, place compute resources for write-heavy workloads within or close to the default leader region.
  • For optimal read performance outside of the default leader region, use staleness of at least 15 seconds.
  • To avoid single-region dependency for the workloads, place critical compute resources in at least two regions.
  • Provision enough compute capacity to keep high priority total CPU utilization under
    • 65% in each region for regional configuration
    • 45% in each region for multi-regional configuration
  • Use the managed autoscaler to automatically adjust capacity based on workload patterns
  • Use tiered storage for cost optimization of historical/cold data
  • Use manual split points to pre-split data before anticipated traffic spikes
  • Leverage the Index Advisor and Schema Recommendations for performance optimization

GCP Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • GCP services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • GCP exam questions are not updated to keep up the pace with GCP updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. Your customer has implemented a solution that uses Cloud Spanner and notices some read latency-related performance issues on one table. This table is accessed only by their users using a primary key. The table schema is shown below. You want to resolve the issue. What should you do?
    1. Remove the profile_picture field from the table.
    2. Add a secondary index on the person_id column.
    3. Change the primary key to not have monotonically increasing values.
    4. Create a secondary index using the following Data Definition Language (DDL) CREATE INDEX person_id_ix ON Persons (
      person_id, firstname, lastname ) STORING ( profile_picture )
  2. You are building an application that stores relational data from users. Users across the globe will use this application. Your CTO is concerned about the scaling requirements because the size of the user base is unknown. You need to implement a database solution that can scale with your user growth with minimum configuration changes. Which storage solution should you use?
    1. Cloud SQL
    2. Cloud Spanner
    3. Cloud Firestore
    4. Cloud Datastore
  3. A financial organization wishes to develop a global application to store transactions happening from different part of the world. The storage system must provide low latency transaction support and horizontal scaling. Which GCP service is appropriate for this use case?
    1. Bigtable
    2. Datastore
    3. Cloud Storage
    4. Cloud Spanner
  4. A company needs to run similarity searches on product embeddings for their recommendation engine while maintaining strong transactional consistency across multiple regions. Which Spanner capability should they use?
    1. Full-text search with search indexes
    2. Vector search with ANN using ScaNN algorithm
    3. Spanner Graph with GQL queries
    4. BigQuery federation with Data Boost
  5. Your organization wants to use Cloud Spanner with 99.999% availability SLA and geo-partitioning to meet data residency requirements. Which Spanner edition should you select?
    1. Standard edition
    2. Enterprise edition
    3. Enterprise Plus edition
    4. Any edition with multi-region configuration
  6. You need to migrate an existing Apache Cassandra application to Cloud Spanner with minimal application code changes. What should you use?
    1. Spanner Migration Tool (SMT)
    2. Dataflow with Spanner connector
    3. Spanner Cassandra interface with CQL compatibility
    4. Manual schema translation and data import
  7. Your team wants to analyze real-time operational data in Spanner without impacting transactional workloads. Which feature should you enable?
    1. Data Boost
    2. BigQuery external datasets
    3. Spanner columnar engine
    4. Change streams with Dataflow
  8. A retail company expects a flash sale that will cause a 10x traffic spike on their Spanner database. What feature can help prepare for this anticipated load?
    1. Managed autoscaler with higher maximum limits
    2. Manual split points to pre-split data for anticipated traffic
    3. Adding more processing units ahead of time
    4. Enabling read leases for all tables

References

Google Cloud Storage & Database Options Comparison

~ Last updated on : ~ jayendrapatil ~ 2 Comments

GCP Storage Options

GCP provides various storage options and the selection can be based on

  • Structured vs Unstructured
  • Relational (SQL) vs Non-Relational (NoSQL)
  • Transactional (OLTP) vs Analytical (OLAP)
  • Fully Managed vs Requires Provisioning
  • Global vs Regional
  • Horizontal vs Vertical scaling

Cloud Firestore

  • Cloud Firestore is a fully managed, highly scalable, serverless, non-relational NoSQL document database
  • fully managed with no-ops and no planned downtime and no need to provision database instances (vs Bigtable)
  • uses a distributed architecture to automatically manage scaling.
  • queries scale with the size of the result set, not the size of the data set
  • supports ACID Atomic transactions – all or nothing (vs Bigtable)
  • provides High availability of reads and writes – runs in Google data centers, which use redundancy to minimize impact from points of failure.
  • provides massive scalability with high performance – uses a distributed architecture to automatically manage scaling.
  • scales from zero to terabytes with flexible storage and querying of data
  • provides SQL-like query language
  • supports strong consistency
  • supports data encryption at rest and in transit
  • provides terabytes of capacity with a maximum unit size of 1 MB per entity (vs Bigtable)
  • Firestore Editions (2025)
    • Standard edition – core Firestore capabilities with standard querying support
    • Enterprise edition – provides MongoDB API compatibility, a new pipeline query engine with 200+ query operations, additional data types, new index types, and text/geospatial search
  • Enterprise Edition Features
    • MongoDB Compatibility (GA Aug 2025) – use existing MongoDB application code, drivers, and tools as a drop-in replacement while getting Firestore’s auto-scaling and high availability
    • Pipeline Query Engine – supports 200+ new query capabilities (pipeline operations) for complex queries directly within the database
    • Text Search and Geospatial Search – native full-text and geospatial query support without external services
    • Maximum document size increased to 16 MiB (Enterprise edition)
    • Indexes are not required for queries in Enterprise edition
  • Consider using Cloud Firestore if you need to store semi-structured objects, or if require support for transactions and SQL-like queries.

Cloud Bigtable

  • Bigtable provides a scalable, fully managed, non-relational NoSQL wide-column analytical big data database service suitable for both low-latency single-point lookups and precalculated analytics.
  • supports large quantities (>1 TB) of semi-structured or structured data (vs Datastore)
  • supports high throughput or rapidly changing data (vs BigQuery)
  • managed, but needs provisioning of nodes and can be expensive (vs Datastore and BigQuery)
  • does not support transactions or strong relational semantics (vs Datastore)
  • Now supports GoogleSQL queries (GA 2024) – familiar SQL syntax for querying Bigtable data directly
  • Not Transactional and does not support ACID
  • provides eventual consistency
  • ideal for time-series or natural semantic ordering data
  • can run asynchronous batch or real-time processing on the data
  • can run machine learning algorithms on the data
  • provides petabytes of capacity with a maximum unit size of 10 MB per cell and 100 MB per row.
  • Bigtable Editions (GA April 2026)
    • Enterprise edition – advanced features in performance, analytic query capability, and resource management
    • Enterprise Plus edition – includes in-memory tier with sub-millisecond latency and hotspot resistance supporting up to 120,000 queries per second on a single row
  • New Features (2024-2026)
    • Bigtable SQL (GoogleSQL) – query data using familiar SQL syntax with specialized features preserving flexible schema
    • Data Boost – serverless analytical queries without impacting operational workloads
    • Incremental Materialized Views – simplify creation of real-time metrics
    • Window Functions (GA April 2026) – advanced analytic operations over multiple table rows
    • KNN Vector Search – K nearest neighbors similarity search for AI/ML use cases
    • Distributed Counting – instant metric retrieval for real-time dashboards
    • In-Memory Tier – hotspot resistance with sub-millisecond latency
    • Agent Skills (April 2026) – let AI agents assist with schema design, SQL queries, and infrastructure management
  • Usage Patterns
    • Low-latency read/write access
    • High-throughput data processing
    • Time series support
  • Anti Patterns
    • Not an ideal storage option for future analysis – Use BigQuery instead
    • Not an ideal storage option for transactional data – Use relational database or Datastore
  • Common Use cases
    • IoT, finance, adtech
    • Personalization, recommendations
    • Monitoring
    • Geospatial datasets
    • Graphs
    • Real-time AI/ML inference and vector search
  • Consider using Cloud Bigtable, if you need high-performance datastore to perform analytics on a large number of structured objects

Cloud Storage

  • Cloud Storage provides durable and highly available object storage.
  • fully managed, simple administration, cost-effective, and scalable service that does not require capacity management
  • supports unstructured data storage like binary or raw objects
  • provides high performance, internet-scale
  • supports data encryption at rest and in transit
  • provides 99.999999999% (11 nines) annual durability
  • Storage Classes: Standard, Nearline (30-day min), Coldline (90-day min), Archive (365-day min)
  • Autoclass – automatically transitions objects to appropriate storage classes based on access patterns
  • New Features (2024-2026)
    • Cloud Storage Rapid (2025-2026) – high-performance storage tier for AI/ML workloads
      • Rapid Bucket (formerly Rapid Storage) – zonal object storage with <1ms random read/write latency, 6 TB/s throughput
      • Rapid Cache (formerly Anywhere Cache) – accelerates reads and colocates compute with data, up to 20 Tbps throughput
    • Smart Storage – automated metadata annotation for unstructured data with AI agent connectivity via MCP
    • Storage Intelligence – zero-configuration dashboards, aggregated activity views, and enhanced batch operations
    • Bucket Relocation – move buckets between regions with minimal downtime
    • Batch Operations Dry Run Mode – simulate batch jobs without modifying data
  • Consider using Cloud Storage, if you need to store immutable blobs larger than 10 MB, such as large images or movies. This storage service provides petabytes of capacity with a maximum unit size of 5 TB per object.
  • Usage Patterns
    • Images, pictures, and videos
    • Objects and blobs
    • Unstructured data
    • Long term storage for archival or compliance
    • AI/ML training data and model checkpoints
  • Anti Patterns
    • Not ideal for structured/relational data
    • Not ideal for frequently changing data requiring low-latency updates
  • Common Use cases
    • Storing and streaming multimedia
    • Storage for custom data analytics pipelines
    • Archive, backup, and disaster recovery
    • AI/ML training datasets and model serving

Cloud SQL

  • provides fully managed, relational SQL databases
  • offers MySQL, PostgreSQL, and SQL Server databases as a service
  • manages OS & Software installation, patches and updates, backups and configuring replications, failover however needs to select and provision machines (vs Cloud Spanner)
  • single region only – although it now supports cross-region read replicas (vs Cloud Spanner)
  • Cloud SQL Editions
    • Enterprise edition – core capabilities, suitable for applications with less stringent availability/performance requirements. Up to 96 vCPU, 624 GB RAM.
    • Enterprise Plus edition – highest performance with optimized software/hardware stack. Up to 128 vCPU, 864 GB RAM. Includes data cache, up to 35-day point-in-time log retention, sub-second maintenance downtime, and advanced disaster recovery.
  • Scaling
    • provides vertical scalability (Max. storage of 64 TB)
    • storage can be increased without incurring any downtime
    • provides an option to increase the storage automatically
    • storage CANNOT be decreased
    • supports Horizontal scaling for read-only using read replicas (vs Cloud Spanner)
    • performance is linked to the disk size
  • Security
    • data is encrypted when stored in database tables, temporary files, and backups.
    • external connections can be encrypted by using SSL, or by using the Cloud SQL Proxy.
    • Private Service Connect (PSC) support for simplified private connectivity
  • High Availability
    • fault-tolerance across zones can be achieved by configuring the instance for high availability by adding a failover replica
    • failover is automatic
    • can be created from primary instance only
    • replication from the primary instance to failover replica is semi-synchronous.
    • failover replica must be in the same region as the primary instance, but in a different zone
    • only one instance for every primary instance allowed
    • supports managed backups and backups are created on primary instance only
    • supports automatic replication
    • Enterprise Plus: sub-second maintenance downtime (vs up to 120 seconds for Enterprise)
  • Backups
    • Automated backups can be configured and are stored for 7 days
    • Manual backups (snapshots) can be created and are not deleted automatically
    • Fast Clone (GA) – clone operations within the same zone for rapid environment creation
  • Point-in-time recovery
    • requires binary logging enabled.
    • every update to the database is written to an independent log, which involves a small reduction in write performance.
    • performance of the read operations is unaffected by binary logging, regardless of the size of the binary log files.
    • Enterprise Plus: up to 35-day log retention (vs 7 days for Enterprise)
  • Usage Patterns
    • direct lift and shift for MySQL, PostgreSQL, SQL Server database only
    • relational database service with strong consistency
    • OLTP workloads
  • Anti Patterns
    • need data storage more than 64 TB or horizontal write scaling, use Cloud Spanner
    • need global availability with low latency, use Cloud Spanner
    • not a direct replacement for Oracle – use installation on GCE or consider AlloyDB for PostgreSQL workloads
  • Common Use cases
    • Websites, blogs, and content management systems (CMS)
    • Business intelligence (BI) applications
    • ERP, CRM, and eCommerce applications
    • Geospatial applications
  • Consider using Cloud SQL for full relational SQL support for OLTP and lift and shift of MySQL, PostgreSQL, SQL Server databases

Cloud Spanner

  • Cloud Spanner provides fully managed, relational SQL databases with joins and secondary indexes
  • provides cross-region, global, horizontal scalability, and availability
  • supports strong consistency, including strongly consistent secondary indexes
  • provides high availability through synchronous and built-in data replication.
  • provides strong global consistency
  • supports database sizes exceeding ~2 TB (vs Cloud SQL)
  • does not provide direct lift and shift for relational databases (vs Cloud SQL)
  • expensive as compared to Cloud SQL
  • Multi-Model Database (2024-2025)
    • Spanner Graph (GA Jan 2025) – supports industry-standard Graph Query Language (GQL) with full SQL interoperability for querying structured and connected data
    • Vector Search – native vector embeddings and similarity search for AI/ML and RAG applications
    • Full-Text Search – native text search capabilities without external services
    • Hybrid Search – combine vector search, full-text search, and ML model reranking in a unified platform
    • Vertex AI Integration – native integration for model serving and inferencing with SQL
  • Spanner Omni (2026 Preview)
    • Self-managed version of Spanner that runs on-premises, across clouds, or on a laptop
    • Brings Spanner’s scalability, high availability, strong consistency, and multi-model capabilities anywhere
    • Supports air-gapped or connected deployments, single machine to clusters of thousands
  • Tiered Storage (GA) – store data across SSD or HDD tiers for cost optimization
  • Consider using Cloud Spanner for full relational SQL support, with horizontal scalability spanning petabytes for OLTP, or as a multi-model database supporting relational, graph, vector, and text search workloads

BigQuery

  • provides fully managed, no-ops, OLAP, enterprise data warehouse (EDW) with SQL and fast ad-hoc queries.
  • provides high capacity, data warehousing analytics solution
  • ideal for big data exploration and processing
  • not ideal for operational or transactional databases
  • provides SQL interface
  • A scalable, fully managed data-to-AI platform
  • BigQuery Editions – Standard, Enterprise, and Enterprise Plus with different pricing and feature tiers
  • New Features (2024-2026)
    • Conversational Analytics (Preview Jan 2026) – analyze data using natural language with AI-powered data agents that understand context and generate SQL
    • BigQuery Graph – uncover complex relationships and patterns in data
    • Vector Search – embeddings and hybrid search for RAG applications
    • BigQuery ML – train and run ML models directly in BigQuery using SQL
    • Data Engineering Agent – automates data preparation, error detection, and pipeline building
    • Data Science Agent – automates data loading, feature engineering, model training and evaluation
    • BigQuery Studio – unified workspace with Gemini-powered assistant for resource discovery and query generation
    • MCP Integration – Model Context Protocol for AI agent connectivity
  • Usage Patterns
    • OLAP workloads up to petabyte-scale
    • Big data exploration and processing
    • Reporting via business intelligence (BI) tools
    • AI/ML model training and inference at scale
  • Anti Patterns
    • Not an ideal storage option for transactional data or OLTP – Use Cloud SQL or Cloud Spanner instead
    • Low-latency read/write access – Use Bigtable instead
  • Common Use cases
    • Analytical reporting on large data
    • Data science and advanced analyses
    • Big data processing using SQL
    • GenAI and agentic AI applications with data

AlloyDB for PostgreSQL

  • AlloyDB is a fully managed, PostgreSQL-compatible database designed for enterprise-grade OLTP and hybrid transactional/analytical (HTAP) workloads
  • wire-compatible with PostgreSQL 14 and 15 – existing drivers, ORMs, and most extensions work without modification
  • provides up to 4x faster for transactional workloads and up to 100x faster for analytical queries compared to standard PostgreSQL
  • uses a scale-out architecture with compute and storage separation
  • built-in AI capabilities with Google’s cutting-edge technology
  • AlloyDB AI
    • Generate vector embeddings from within the database
    • Native vector search with up to 10x faster index creation and 4x faster search queries
    • Filtered vector search up to 10x faster than standard PostgreSQL HNSW
    • Integration with Vertex AI for model serving and inferencing
    • AlloyDB AI query engine with Vertex AI Ranking API
  • AlloyDB Omni – downloadable version that runs on-premises or in other clouds
  • 99.99% availability SLA with automated backups, replication, and failover
  • Usage Patterns
    • Enterprise PostgreSQL workloads requiring high performance
    • HTAP (hybrid transactional/analytical) workloads
    • AI-powered applications requiring vector search
    • Migration from commercial databases (Oracle, SQL Server) to PostgreSQL
  • Anti Patterns
    • Need global horizontal scaling – Use Cloud Spanner
    • Need non-relational/NoSQL – Use Firestore or Bigtable
    • Need MySQL or SQL Server compatibility – Use Cloud SQL
  • Consider using AlloyDB for PostgreSQL workloads requiring high performance, AI integration, or migration from commercial databases

Memorystore

  • provides scalable, secure, and highly available in-memory service
  • fully managed as provisioning, replication, failover, and patching are all automated
  • is protected from the internet using VPC networks and private IP and comes with IAM integration
  • Supported Engines
    • Memorystore for Valkey (GA 2025) – open-source, high-performance key-value store (successor to Redis OSS). Supports Valkey 8.0 and 9.0. 99.99% availability SLA, instances up to 14.5 TB, cross-region replication, Private Service Connect, multi-VPC access.
    • Memorystore for Redis Cluster – managed Redis cluster mode with zero-downtime scaling
    • Memorystore for Redis – standard Redis instances (standalone and high availability)
    • Memorystore for Memcached – distributed in-memory caching
  • Valkey 9.0 Features (GA 2026)
    • SIMD optimizations for improved throughput and latency
    • Enhanced performance over previous versions
    • Full compatibility with Redis OSS commands
  • Usage Patterns
    • Lift and shift migration of applications
    • Low latency data caching and retrieval
    • Session management
    • Real-time leaderboards and counting
  • Anti Patterns
    • Relational or NoSQL database
    • Analytics solution
    • Persistent primary data store (use as cache layer)
  • Common Use cases
    • User session management
    • Application caching
    • Real-time analytics and pub/sub
    • Gaming leaderboards

GCP Storage Options Decision Tree

GCP Storage Options Decision Tree

GCP Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • GCP services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • GCP exam questions are not updated to keep up the pace with GCP updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. Your application is hosted across multiple regions and consists of both relational database data and static images. Your database has over 10 TB of data. You want to use a single storage repository for each data type across all regions. Which two products would you choose for this task? (Choose two)
    1. Cloud Bigtable
    2. Cloud Spanner
    3. Cloud SQL
    4. Cloud Storage
  2. You are building an application that stores relational data from users. Users across the globe will use this application. Your CTO is concerned about the scaling requirements because the size of the user base is unknown. You need to implement a database solution that can scale with your user growth with minimum configuration changes. Which storage solution should you use?
    1. Cloud SQL
    2. Cloud Spanner
    3. Cloud Firestore
    4. Cloud Datastore
  3. Your company processes high volumes of IoT data that are time-stamped. The total data volume can be several petabytes. The data needs to be written and changed at a high speed. You want to use the most performant storage option for your data. Which product should you use?
    1. Cloud Datastore
    2. Cloud Storage
    3. Cloud Bigtable
    4. BigQuery
  4. Your App Engine application needs to store stateful data in a proper storage service. Your data is non-relational database data. You do not expect the database size to grow beyond 10 GB and you need to have the ability to scale down to zero to avoid unnecessary costs. Which storage service should you use?
    1. Cloud Bigtable
    2. Cloud Dataproc
    3. Cloud SQL
    4. Cloud Firestore (Datastore mode)
  5. A financial organization wishes to develop a global application to store transactions happening from different part of the world. The storage system must provide low latency transaction support and horizontal scaling. Which GCP service is appropriate for this use case?
    1. Bigtable
    2. Datastore
    3. Cloud Storage
    4. Cloud Spanner
  6. You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?
    1. Cloud SQL
    2. Cloud Bigtable
    3. Cloud Spanner
    4. Cloud Datastore

    Note: With Cloud SQL now supporting up to 64 TB, Cloud SQL could also be a valid option for 20 TB. However, for operational transactional data requiring high scalability, Cloud Spanner remains the better choice.

  7. Your team needs a PostgreSQL-compatible database that can handle both transactional and analytical queries with high performance. The application also requires built-in vector search capabilities for an AI-powered recommendation engine. Which GCP service should you choose?
    1. Cloud SQL for PostgreSQL
    2. AlloyDB for PostgreSQL
    3. Cloud Spanner
    4. BigQuery
  8. Your company is building a real-time fraud detection system that needs to query relationships between entities (accounts, transactions, merchants) while also performing vector similarity searches on transaction patterns. The system must provide strong consistency and global availability. Which database should you use?
    1. Cloud Bigtable
    2. BigQuery
    3. Cloud Spanner
    4. Cloud Firestore
  9. Your organization is migrating from MongoDB to Google Cloud. You want to minimize code changes and use existing MongoDB drivers and tools. The application requires automatic scaling and high availability. Which GCP service should you use?
    1. Cloud SQL for PostgreSQL
    2. Cloud Bigtable
    3. Cloud Firestore (Enterprise edition with MongoDB compatibility)
    4. AlloyDB for PostgreSQL
  10. You need a high-performance caching layer for your microservices application on GCP. The solution must support cross-region replication, provide 99.99% availability, and be compatible with open-source tooling. Which service should you choose?
    1. Memorystore for Redis
    2. Cloud CDN
    3. Memorystore for Valkey
    4. Cloud Firestore

See also: Google Cloud Storage Services Cheat Sheet